{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fc87356-a9d1-4de4-87a9-1ffcc27e7bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "x,y=load_breast_cancer().data,load_breast_cancer().target\n",
    "x=StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "112430f9-5ced-4f99-afdd-60bbfe2f6d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优参数值为：{'coef0': np.float64(0.0), 'gamma': np.float64(0.18329807108324375)}\n",
      "选取该参数值时，模型的预测准确率为：0.969591\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "gamma_range=np.logspace(-10,1,20)\n",
    "coef0_range=np.linspace(0,5,10)\n",
    "param_grid=dict(gamma=gamma_range,coef0=coef0_range)\n",
    "cv=StratifiedShuffleSplit(n_splits=5,test_size=0.3,random_state=420)\n",
    "grid=GridSearchCV(SVC(kernel=\"poly\",degree=1),param_grid=param_grid,cv=cv)\n",
    "grid.fit(x,y)\n",
    "print(\"最优参数值为：%s\"%grid.best_params_)\n",
    "print(\"选取该参数值时，模型的预测准确率为：%f\"%grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a58ed907-d793-4115-a79c-ed1858ceb6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数gamma的最优值为：0.012067926406393264\n",
      "选取该参数值时，模型的预测准确率为：0.976608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=420)\n",
    "score=[]\n",
    "gamma_range=np.logspace(-10,1,50)\n",
    "for i in gamma_range:\n",
    "    model=SVC(kernel='rbf',gamma=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred=model.predict(x_test)\n",
    "    ac=accuracy_score(y_test,pred)\n",
    "    score.append(ac)\n",
    "plt.plot(gamma_range,score)\n",
    "plt.show()\n",
    "print(\"参数gamma的最优值为：%s\"%gamma_range[score.index(max(score))])\n",
    "print(\"选取该参数值时，模型的预测准确率为：%f\"%max(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "630d9051-1eaf-4549-8514-f19d8e9dbfc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数gamma的最优值为：1.2340816326530613\n",
      "选取该参数值时，模型的预测准确率为：0.976608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=420)\n",
    "score=[]\n",
    "C_range=np.linspace(0.01,30,50)\n",
    "for i in C_range:\n",
    "    model=SVC(kernel='linear',C=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred=model.predict(x_test)\n",
    "    ac=accuracy_score(y_test,pred)\n",
    "    score.append(ac)\n",
    "plt.plot(C_range,score)\n",
    "plt.show()\n",
    "print(\"参数gamma的最优值为：%s\"%C_range[score.index(max(score))])\n",
    "print(\"选取该参数值时，模型的预测准确率为：%f\"%max(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55cf8f54-dbb3-46d9-b90e-31fc2ac5fe0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数gamma的最优值为：6.7424489795918365\n",
      "选取该参数值时，模型的预测准确率为：0.982456\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=420)\n",
    "score=[]\n",
    "C_range=np.linspace(0.01,30,50)\n",
    "for i in C_range:\n",
    "    model=SVC(kernel='rbf',C=i,gamma=0.01207)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred=model.predict(x_test)\n",
    "    ac=accuracy_score(y_test,pred)\n",
    "    score.append(ac)\n",
    "plt.plot(C_range,score)\n",
    "plt.show()\n",
    "print(\"参数gamma的最优值为：%s\"%C_range[score.index(max(score))])\n",
    "print(\"选取该参数值时，模型的预测准确率为：%f\"%max(score))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
